Method and device for time-of-flight estimate

ABSTRACT

A method for providing an estimate of a time-of-flight between an ultrasonic signal emitted by a device and an ultrasonic echo signal returned by a target object hit by the ultrasonic signal and received at the device. The method includes acquiring the ultrasonic echo signal thereby obtaining an electric echo signal; determining a noise power of the electric echo signal; determining an envelope signal indicative of an envelope of the electric echo signal; determining a portion of the envelope signal based on at least one operative parameter, the at least one operative parameter being determined according to Particle Swarm Optimization; processing the portion of the envelope signal and the noise power of the echo ultrasonic signal according to an Unscented Kalman Filter to obtain an estimate of the envelope signal, wherein the estimate of the envelope signal is a regenerated version of the envelope signal being regenerated from the portion of the envelope signal, the processing being based on at least one Unscented Kalman Filter parameter determined according to the Particle Swarm Optimization, and providing the estimate of the time-of-flight according to the estimate of the envelope signal.

BACKGROUND Technical Field

The present disclosure relates to a device for ultrasonic time-of-flight(TOF) estimate (hereinafter, TOF device) and a corresponding methodimplemented by the TOF device.

Description of the Related Art

According to ultrasonic TOF estimate principles, an ultrasonic signal(hereinafter, ultrasonic source signal) is generated and transmitted bythe TOF device towards a target body, a corresponding ultrasonic signal(hereinafter, ultrasonic echo signal) originated from the target body byreflection of the ultrasonic source signal hitting the target body isreceived at the TOF device, and the TOF estimate is determined as thetime elapsed from the transmission of the ultrasonic source signal andthe reception of the ultrasonic echo signal.

In typical applications, e.g., in applications for obstacle detection,the TOF device may be configured to determine, e.g., based on the TOFestimate, a distance estimate indicative of a distance between the TOFdevice and the target body.

The output of the TOF device may be the distance estimate, and/or thedistance estimate may be part of an additional information based on thedistance estimate, such as displacement information, level information,material information, structure information, vibration information, andmedical diagnostic information.

According to a known implementation, the TOF estimate is based on KalmanFilter, which is an algorithm that, given a set of measures, generatesan optimal estimate of desired quantities through a recursiveprocessing.

Extended Kalman Filter is also known, which applies to non-linearsystems. Basically, the Extended Kalman Filter provides for alinearization of a non-linear system through Jacobian computation.

Unscented Kalman Filter is also known, which also applies to non-linearsystems. Basically, the Unscented Kalman Filter provides for alinearization of probabilistic distributions of an error.

Nowadays, TOF estimate based on the Unscented Kalman Filter is apreferred choice.

BRIEF SUMMARY

The Applicant has understood that TOF estimate based on the UnscentedKalman Filter has some drawbacks.

For example, the Applicant has understood that the performances of theUnscented Kalman Filter are closely related to an efficient calibrationof a large number of parameters (including parameters associated with anacquisition of the ultrasonic echo signal and parameters of theUnscented Kalman Filter).

Due to the large number of parameters to be calibrated and since thecalibration of these parameters is almost always done by hand accordingto designer experience, the performances of the Unscented Kalman Filtermay often be below expectations.

Moreover, the Applicant has also understood that, during TOF devicelifetime, the TOF estimates may also be affected by external conditions(such as environmental conditions). For example, changes in airtemperature, humidity, air pressure, air turbulence, external noise mayheavily affect an ultrasonic signal acquisition, so that parametercalibration could result inadequate as a result of changed externalconditions.

Last but not least, the Applicant has understood that high computationalcomplexity of current TOF estimate methods does not allow efficientimplementation in available microcontrollers.

The Applicant has faced the above-mentioned issues, and has devised amethod for TOF estimate and a corresponding TOF device that allowseasily and dynamically tuning (i.e., adjusting or updating or refiningor calibrating) the parameters associated with the processing of theacquired ultrasonic echo signal and the parameters of the UnscentedKalman Filter.

For example, an aspect of the present disclosure relates to a method forproviding an estimate of a time-of-flight between an ultrasonic signalemitted by a device and an ultrasonic echo signal returned by a targetobject hit by the ultrasonic signal and received at the device. Themethod may comprise acquiring the ultrasonic echo signal therebyobtaining an electric echo signal. The method may comprise determining anoise power of the electric echo signal. The method may comprisedetermining an envelope signal indicative of an envelope of the electricecho signal. The method may comprise determining a portion of theenvelope signal based on at least one operative parameter; said at leastone operative parameter may be determined according to Particle SwarmOptimization. The method may comprise processing the portion of theenvelope signal and the noise power of the echo ultrasonic signalaccording to an Unscented Kalman Filter to obtain an estimate of theenvelope signal; the estimate of the envelope signal may be aregenerated version of the envelope signal being regenerated from theportion of the envelope signal; said processing may for example be basedon at least one Unscented Kalman Filter parameter (UKFP_(K)) determinedaccording to the Particle Swarm Optimization. The method may compriseproviding said estimate of the time-of-flight according to the estimateof the envelope signal.

According to an embodiment, whose features are additional or alternativeto one or more features of any of the previous embodiments, the methodcomprises determining an estimate error. The Particle Swarm Optimizationmay be based on said estimate error.

According to an embodiment, whose features are additional or alternativeto one or more features of any of the previous embodiments saiddetermining an estimate error comprises determining a difference betweenthe estimate of envelope signal and the envelope signal.

According to an embodiment, whose features are additional or alternativeto one or more features of any of the previous embodiments, the methodcomprises, based on said estimate of the time-of-flight, determining adistance estimate indicative of a distance between the target object andthe device. Said determining an estimate error may comprise determininga difference between the distance estimate and said distance.

According to an embodiment, whose features are additional or alternativeto one or more features of any of the previous embodiments, saiddetermining an envelope signal comprises performing a Hilberttransformation on the echo ultrasonic signal.

According to an embodiment, whose features are additional or alternativeto one or more features of any of the previous embodiments, the portionof the envelope signal is centered about a maximum value of the envelopesignal.

According to an embodiment, whose features are additional or alternativeto one or more features of any of the previous embodiments, saidprocessing the operative portion of the envelope signal comprisesproviding a regenerated envelope signal.

According to an embodiment, whose features are additional or alternativeto one or more features of any of the previous embodiments, said atleast one operative parameter comprises at least one among:

-   -   operative parameters indicative of a maximum length of the        portion of the envelope signal over time, and    -   operative parameters indicative of an optimized length of the        portion of the envelope signal over time, the optimized length        being lower than the maximum length.

According to an embodiment, whose features are additional or alternativeto one or more features of any of the previous embodiments, said atleast one Unscented Kalman Filter parameter comprises at least oneamong:

-   -   an evaluation parameter providing a rough estimate of the        time-of-flight;    -   a control parameter for controlling the spread of sigma points        around a mean state value, and    -   a correction parameter providing a correction on the noise power        of the electric echo signal.

Another aspect of the present disclosure relates to a device forproviding an estimate of a time-of-flight between an ultrasonic signalemitted by the device and an ultrasonic echo signal returned by a targetobject hit by the ultrasonic signal and received at the device. Thedevice may comprise a conditioning and conversion system for acquiringthe ultrasonic echo signal thereby obtaining an electric echo signal.The device may comprise a module for determining a noise power of theelectric echo signal. The device may comprise a module for determiningan envelope signal indicative of an envelope of the electric echosignal. The device may comprise a module for determining a portion ofthe envelope signal based on at least one operative parameter; said atleast one operative parameter may be determined according to ParticleSwarm Optimization. The device may comprise a module for processing theportion of the envelope signal and the noise power of the echoultrasonic signal according to an Unscented Kalman Filter to obtain anestimate of the envelope signal; the estimate of the envelope signal maybe a regenerated version of the envelope signal being regenerated fromthe portion of the envelope signal; said processing may be based on atleast one Unscented Kalman Filter parameter determined according to theParticle Swarm Optimization. The device may comprise a module forproviding said estimate of the time-of-flight according to the estimateof the envelope signal.

A further aspect of the present disclosure relates to an electronicsystem comprising such a device (or more thereof).

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

These and other features and advantages of the present disclosure willbe made apparent by the following description of some exemplary andnon-limitative embodiments thereof; for its better intelligibility, thefollowing description should be read making reference to the attacheddrawings, wherein:

FIG. 1 schematically shows a device for ultrasonic time-of-flightestimate, according to an embodiment of the present disclosure;

FIGS. 2A and 2B show activity diagrams of methods according torespective embodiments of the present disclosure;

FIG. 3 shows a simplified block diagram of an electronic systemcomprising the device of FIG. 1 according to an embodiment of thepresent disclosure;

FIGS. 4 and 5 show activity diagrams of respective methods according torespective embodiments of the present disclosure.

DETAILED DESCRIPTION

With reference to the drawings, FIG. 1 schematically shows a device(hereinafter, TOF device) 100 for providing an ultrasonic time-of-flightestimate (hereinafter, TOF estimate), according to an embodiment of thepresent disclosure. The TOF device 100 is configured to implement amethod (hereinafter, TOF method) for providing the TOF estimate.

In the following, when one or more features of the TOF device and of theTOF method are introduced by the wording “according to an embodiment”,they are to be construed as features additional or alternative to anyfeatures previously introduced, unless otherwise indicated and/or unlessthere is evident incompatibility among feature combinations.

In the following, only components of (and TOF method steps performed by)the TOF device 100 deemed relevant for the understanding of the presentdisclosure will be shown and discussed, with other well-known componentsof (and TOF method steps performed by) the TOF device 100 that will beintentionally omitted for the sake of conciseness.

According to ultrasonic time-of-flight estimate principles, anultrasonic signal (hereinafter, ultrasonic source signal) USS isgenerated and transmitted by the TOF device 100 towards a target body T(the target body being eternal to, i.e., not part of, the TOF device100), a corresponding ultrasonic signal (hereinafter, ultrasonic echosignal) UES originated from the target body T by reflection of theultrasonic source signal USS hitting the target body T is received atthe TOF device 100, and the TOF estimate is determined (by the TOFdevice 100) as the time elapsed from the transmission of the ultrasonicsource signal USS and the reception of the ultrasonic echo signal UES.

According to an embodiment, the TOF device 100 is configured todetermine, e.g., based on the TOF estimate, a distance estimate D_(EST)indicative of a distance D_(ACT) between the TOF device 100 and thetarget body T.

According to an embodiment, the TOF device 100 may be configured tofurther determine, e.g., based on the TOF estimate and/or on thedistance estimate D_(EST), one or more additional information. Accordingto an embodiment, as better discussed in the following, the TOF device100 may be part of an electronic system aimed at determining theadditional information based on the TOF estimate and/or on the distanceestimate D_(EST) provided by the TOF device 100.

Examples of additional information include, but are not limited to,displacement information, level information, material information,structure information, vibration information, and medical diagnosticinformation.

For the purposes of the present disclosure, the target body T (which isnot part of the TOF device 100) comprises a physical object with mass.Examples of target bodies include, but are not limited to, living beings(such as persons, animals and trees) or parts thereof, and inanimateobjects (such as buildings and vehicles) or parts thereof.

According to an embodiment, the TOF device 100 comprises an ultrasonictransducer 105. According to an embodiment, the ultrasonic transducer105 comprises a piezoelectric ultrasonic transducer or a capacitiveultrasonic transducer.

According to an embodiment, the ultrasonic transducer 105 is configuredto transduce an electric source signal ESS (e.g., a pulse-widthmodulated pulse train) into the ultrasonic source signal USS, and totransduce the ultrasonic echo signal UES from the target body T toobtain a corresponding electric echo signal EES.

According to an embodiment, the electric source signal ESS and theelectric echo signal EES are digital signals, the ultrasonic transducer105 for example comprising a conditioning and conversion system (notshown) for obtaining, from the (digital) electric source signal ESS, ananalog ultrasonic source signal to be transduced into the electric echosignal EES, and for obtaining the (digital) electric echo signal EESfrom the transduced ultrasonic echo signal.

According to an embodiment, the TOF device 100 comprises a processingunit 110 (for example, a microcontroller and/or a microprocessor)electrically coupled to the ultrasonic transducer 105 for providing theelectric source signal ESS thereto and for receiving the electric echosignal EES therefrom.

In the following, only relevant modules of the processing unit 110 thatare deemed pertinent for the understanding of the present disclosurewill be discussed, with well-known and/or obvious variants of therelevant modules that are omitted for the sake of conciseness.

The term “module” is herein intended to emphasize functional (ratherthan implementation) aspects thereof. Indeed, without losing generality,each module, according to its function, may be implemented by software,hardware, and/or a combination thereof. Moreover, the modules (or atleast a subset thereof) may also reflect, at least conceptually, thephysical structure of the processing unit. In any case, it will beappreciated that one or more of the illustrated modules may beintegrated together in a single electronic unit.

According to an embodiment, the processing unit 110 comprises a module115 for determining a noise power NP (or an indication thereof)associated with the electric echo signal EES.

According to an embodiment, in order to determine the noise power NP (oran indication thereof) associated with the electric echo signal EES, themodule 115 is configured to process the electric echo signal EESaccording to Fourier transform. In the description herein, the module115 is also referred to as Fourier module 115.

According to an embodiment, the processing unit 110 comprises a module120 for determining an envelope signal EES_(ENV) indicative of anenvelope (e.g., a profile) of the echo ultrasonic signal.

According to an embodiment, in order to determine the envelope signalEES_(ENV), the module 120 is configured to process the electric echosignal EES according to Hilbert transform. In the description herein,the module 120 is also referred to as Hilbert module 120.

Since the frequency of the envelope signal EES_(ENV) is lower than thefrequency of the electric echo signal EES, the envelope signal EES_(ENV)can be appropriately decimated in terms of sample/s without violatingNyquist requests. Thus, the Hilbert module 120 performs a first signal“dilution” without loss of information content, which determines lowcomputational requests for the processing unit 110. Just as an example,the frequency of the electric echo signal EES may be about 400 KHz andthe frequency of the envelope signal EES_(ENV) may be about 25 KHz.

According to an embodiment, the processing unit 110 comprises a module125 for determining a portion of the envelope signal EES_(ENV). In thedescription herein, the module 125 is also referred to as portion module125 and the portion of the envelope signal EES_(ENV) is also referred toas envelope signal portion EES_(ENVp).

According to an embodiment, the envelope signal portion EES_(ENVp)comprises a portion of the envelope signal EES_(ENV) comprising amaximum value of the envelope signal EES_(ENV), i.e., a portion of theenvelope signal EES_(ENV) at the left of the maximum value (hereinafter,left portion of the envelope signal EES_(ENV)) and a portion of theenvelope signal EES_(ENV) at the right of the maximum value(hereinafter, right portion of the envelope signal EES_(ENV)). The leftportion of the envelope signal EES_(ENV) and the right portion of theenvelope signal EES_(ENV) may have same time lengths, e.g., said portionof the envelope signal EES_(ENV) is centered around said maximum valueof the envelope signal EES_(ENV), or may have different time lengths,e.g., said portion of the envelope signal EES_(ENV) is not centeredaround said maximum value of the envelope signal EES_(ENV).

As the envelope signal portion EES_(ENVp) is a portion of the envelopesignal EES_(ENV), the envelope signal portion EES_(ENVp) can be quicklyprocessed by the following modules of the processing unit 110 (so as toobtain a fast TOF estimate, and hence a fast distance estimate D_(EST)).Thus, the portion module 125 performs a second signal “dilution” withoutloss of information content, which determines low computational requestsfor the processing unit 110.

Thanks to the low computational requests for the processing unit 110,the processing unit 110 may be a conventional microcontroller availableon the market.

According to an embodiment, the envelope signal portion EES_(ENVp) isdetermined based on one or more operative parameters.

Examples of operative parameters comprise, but are not limited to:

-   -   a first operative parameter Perc_l indicative of a maximum        length of the left portion of the envelope signal EES_(ENV). In        other words, the first operative parameter Perc_l is indicative        of an admitted extent of the left portion of the envelope signal        EES_(ENV) over the abscissae axis (i.e., over time), and may for        example depend on processing capabilities of the following        modules of the processing unit 110 and/or on other design        options;    -   a second operative parameter Perc_u indicative of a maximum        length of the right portion of the envelope signal EES_(ENV). In        other words, the second operative parameter Perc_u is indicative        of an admitted extent of the right portion of the envelope        signal EES_(ENV) over the abscissae axis (i.e., over time), and        may for example depend on processing capabilities of the        following modules of the processing unit 110 and/or on other        design options (therefore, the first and second operative        parameters are indicative of a maximum length of the envelope        signal portion EES_(ENVp) over time);    -   a third operative parameter Coeff1 indicative of an optimized        length of the left portion of the envelope signal EES_(ENV). In        other words, the third operative parameter Coeff1 is indicative        of an optimized extent (lower than the admitted extent) of the        left portion of the envelope signal EES_(ENV) over the abscissae        axis (i.e., over time). Compared to the admitted extent, the        optimized length of the left portion of the envelope signal        EES_(ENV) can further reduce the computation requested by the        following modules of the processing unit 110 without losing        information content;    -   a fourth operative parameter Coeff1 indicative of an optimized        length of the right portion of the envelope signal EES_(ENV). In        other words, the fourth operative parameter Coeff1 is indicative        of an optimized extent (lower than the admitted extent) of the        right portion of the envelope signal EES_(ENV) over the        abscissae axis. Therefore, the third and fourth operative        parameters Coeff1, Coeff1 are indicative of an optimized length        of the envelope signal portion EES_(ENVp) over time. Compared to        the admitted extent, the optimized length of the right portion        of the envelope signal EES_(ENV) can further reduce the        computation requested by the following modules of the processing        unit 110 without losing information content.

In the following, when distinguishing among the first, second, third andfourth operative parameters is not relevant for understanding thedisclosure the operative parameters will be globally denoted by OP_(k),wherein k denotes the iteration of the TOF method—indeed, as betterdiscussed in the following, according to an embodiment the operativeparameters, or at least a subset thereof, are tuned or adjusted orupdated at each iteration of the TOF method.

According to an embodiment, the operative parameters, or a subsetthereof, are determined according to Particle Swarm Optimization, asdiscussed herein in details.

According to an embodiment, the portion module 125, better discussed inthe following, is configured to determine the envelope signal portionEES_(ENVp) by applying to the envelope signal EES_(ENV) the first andsecond operative parameters Perc_l, Perc_u at a first phase, and thethird and fourth operative parameters Coeff1, Coeff2 at a second phase(following the first phase) aimed at optimizing the length of theenvelope signal portion EES_(ENVp).

According to an embodiment, the processing unit 110 comprises a module130 for processing the envelope signal portion EES_(ENVp) and the noisepower NP of the electric echo signal EES according to an UnscentedKalman Filter (reason why, in the following, the module 130 will bereferred to as UKF (“Unscented Kalman Filter”) module 130).

Kalman filter is an algorithm that uses a series of measurementsobserved over time, containing statistical noise and other inaccuracies,and produces estimates of unknown variables that tend to be moreaccurate than those based on a single measurement alone, by estimating ajoint probability distribution over the variables for each timeframe.

The Kalman filter keeps track of the estimated state of the system andthe variance or uncertainty of the estimate. The estimate is updatedusing a state transition model and measurements.

The algorithm works in a two-step process. In a prediction step, theKalman filter produces estimates of the current state variables, alongwith their uncertainties. Once the outcome of the next measurement,possibly corrupted with some amount of error, including random noise, isobserved, these estimates are updated using a weighted average, withmore weight being given to estimates with higher certainty. Thealgorithm is recursive. It can run in real time, using only the presentinput measurements and the previously calculated state and itsuncertainty matrix; no additional past information is required.

Unscented Kalman filter is a generalization of the Kalman filter whichworks on nonlinear systems. In the UKF, the probability density isapproximated by a deterministic sampling of points which represent theunderlying distribution as a Gaussian. The nonlinear transformation(referred to as unscented transformation) of these points are intendedto be an estimate of the posterior distribution, the moments of whichcan then be derived from the transformed samples.

According to an embodiment, the UKF module 130 is configured to processthe envelope signal portion EES_(ENVp) and the noise power NP of theelectric echo signal based on one or more UKF parameters (the UKFparameters, or a subset thereof, being determined according to theParticle Swarm Optimization, as better discussed in the following).

Examples of UKF parameters comprise, but are not limited to anevaluation parameter (“to_md_capture”) providing a first, roughevaluation of the TOF estimate (so as to provide a good starting pointto the UKF module 130), a control parameter (“Kappa_p”) for controllingthe spread of the sigma points around the mean state value, and acorrection parameter (“powerNoiseCorr”) providing a correction on thenoise power NP. In some embodiments, the correction parameter being forexample an additive correction factor to be added to the noisecovariance matrix associated with the electric echo signal EES. In otherwords, the “to_md_capture” parameter is indicative of an approximateestimation of the time-of-flight used as a starting point for thetime-of-flight calculation and is proportional to x_max_inv−3σ, thex_max_inv being described in the following and the a being a predefinedvalue linked to the standard deviation of a Gaussian curve approximatingthe envelope signal portion EES_(ENV,p), and the “Kappa_p” parameter isindicative of the spread of the sigma points around a mean value of thestate variable of the UKF.

as described herein, the UKF parameters will be globally denoted byUKFP_(k), wherein k denotes the iteration of the TOF method. As furtherdiscussed herein, according to an embodiment the UKF parameters aretuned or adjusted or updated at each iteration of the TOF method.

According to an embodiment, the UKF module 130 is configured to processthe envelope signal portion EES_(ENVp) and the noise power NP of theelectric echo signal EES to obtain an estimate of the envelope signalEES_(ENV)(hereinafter referred to as envelope signal estimateEES_(ENVest)). According to an embodiment, the envelope signal estimateEES_(ENVest) is a regenerated version of the envelope signal EES_(ENV)being regenerated from the envelope signal portion EES_(ENVp) (see, forexample, L. Angrisani, A. Baccigalupi, R. Schiano Lo Moriello,“Ultrasonic time-of-flight estimation through unscented Kalman filter”,IEEE Transactions on Instrumentation and Measurement, August 2006).

According to an embodiment, the processing unit 110 comprises a module(hereinafter, evaluation module) 135 for determining the TOF estimateaccording to the envelope signal estimate EES_(ENVest).

According to an embodiment, the TOF estimate is based on the followingdiscrete-time expression modelling the ultrasonic signal envelope (see,for example, L. Angrisani, A. Baccigalupi, R. Schiano Lo Moriello,“Ultrasonic time-of-flight estimation through unscented Kalman filter”,IEEE Transactions on Instrumentation and Measurement, August 2006):

${A\left( {kt}_{s} \right)} = {{A_{0}\left( \frac{{kt}_{s} - \tau}{T} \right)}^{\alpha}e^{(\frac{{kt}_{s} - \tau}{T})}}$

wherein:

-   -   A₀ is the amplitude of the electric echo signal EES;    -   α and T are parameters depending on the adopted ultrasonic        transducer;    -   τ is the TOF estimate, and    -   t_(s) is the sampling period.

According to an embodiment, the evaluation module 135 is configured todetermine, according to the TOF estimate, the distance estimate D_(EST)between the target object T and the TOF device 100.

According to an embodiment, the processing unit 110 comprises a module(hereinafter, error module) 140 for determining an estimate error e. Asbetter discussed in the following, the operative parameters OP_(k) (or asubset thereof) and the UKF parameters UKFP_(k) (or a subset thereof)are determined according to the Particle Swarm Optimization receivingthe estimate error ε as input).

According to an embodiment, the estimate error e determined at the errormodule 140 comprises a difference between the distance estimate D_(EST)and the distance D_(ACT) (i.e., the actual distance) between the TOFdevice 100 and the target body T. As will be better understood from thefollowing discussion, this embodiment allows iteratively adjusting,tuning, updating or refining the operative parameters OP_(k) (or asubset thereof) and the UKF parameters UKFP_(k) at a preliminary orcalibrating phase of the TOF device 100 (i.e., a phase that precedes theuse of the TOF device 100 as a meter, e.g., as a distance meter). Asbetter discussed in the following, this preliminary or calibrating phaseof the TOF device 100 is obtained through an embodiment of the TOFmethod (hereinafter referred to as “offline TOF method”), in which aknown target body T placed at a known distance D_(ACT) is used to setthe operative parameters OP_(k) and the UKF parameters UKFP_(k) to besubsequently used by the TOF device 100 when used as a meter (e.g., adistance meter).

According to an embodiment, the estimate error e determined at the errormodule 140 comprises a difference between the envelope signal estimateEES_(ENVest) and the envelope signal EES_(ENV). As will be betterunderstood from the following discussion, this embodiment allowsiteratively adjusting, tuning, updating or refining a subset of theoperative parameters OP_(k) and a subset of the UKF parameters UKFP_(k)in real-time during the use of the TOF device 100 as a meter (e.g., as adistance meter). As better discussed in the following, this is obtainedthrough an embodiment of the TOF method (hereinafter referred to as“online TOF method”).

According to an embodiment, the TOF device 100 may be configured toimplement the offline TOF method (in which case the distance estimateD_(EST) and the distance D_(ACT) are received at the error module 140),or the online TOF method (in which case the envelope signal EES_(ENV)and the envelope signal estimate EES_(ENVest) are received at the errormodule 140), or both the offline and online TOF methods (for example,with the online TOF method that may follow the offline TOF method): thepossibility of implementing the offline TOF method and/or the online TOFmethod is conceptually represented in FIG. 1 by dashed arrows associatedthe distance estimate D_(EST), the distance D_(ACT), the envelope signalEES_(ENV) and the envelope signal estimate EES_(ENVest) being input tothe error module 140.

According to an embodiment, the processing unit 110 comprises a module(hereinafter, Swarm module) 145 for determining the operative parametersOP_(k) (or the subset of operative parameters OP_(k)) and the UKFparameters UKFP_(k) (or the subset of UKF parameters UKFP_(k)) accordingto the Particle Swarm Optimization and based on the estimate error Ereceived as input by the error module 140.

Particle Swarm Optimization is a computational method that optimizes aproblem by iteratively trying to improve a candidate solution withregard to a given measure of quality. It solves a problem by having apopulation of candidate solutions, called particles, and moving theseparticles around in the search-space according to simple mathematicalformulae over the particle's position and velocity. Each particle'smovement is influenced by its local best known position, but is alsoguided toward the best known positions in the search-space, which areupdated as better positions are found by other particles.

The main equations of the Particle Swarm Optimization are the following:

C _(i,j) =c ₁ r _(1j)(p _(i,j)(t−1)−x _(i,j)(t−1))

S _(j) =c ₂ r _(2,j)(g _(i,j)(t−1)−x _(i,j)(t−1))

V _(i,j)(t)=wV _(i,j)(t−1)+C _(i,j) +S _(i,j)

x _(i,j)(t)=x _(i,j)(t−1)+V _(i,j)(t)

wherein:

-   -   p_(i,j) is the local best of the i-th particle;    -   g_(i,j) is the global best of i-th particle in the current        neighborhood;    -   C_(i,j) is the i-th cognitive parameter in j-dimensional search        space;    -   S_(i,j) is the i-th social parameter in j-dimensional search        space;    -   V_(i,j)(t) is the i-th particle velocity parameter in        j-dimensional search space;    -   x_(i,j) (t) is the i-th particle position (solution) in        j-dimensional search space;    -   r_(1,j) r_(2,j) are uniformly distributed random values in        [0,1];    -   c₁, c₂ are acceleration coefficients for both cognitive and        social components; and    -   w is the inertia weight aimed at judging between exploring and        exploiting phases.

FIG. 2A shows an activity diagram of an offline TOF method 200 _(A)according to embodiments of the present disclosure.

According to an embodiment, the offline TOF method 200 _(A) isimplemented by proper software instructions stored in or accessible bythe TOF device 100, and/or by proper hardware/firmware of the TOF device100.

According to an embodiment, the offline TOF method 200 _(A) comprisesacquiring the ultrasonic echo signal UES thereby obtaining thecorresponding electric echo signal EES (action node 205). According toan embodiment, the acquisition of the ultrasonic echo signal UES toobtain the corresponding electric echo signal EES is performed at theconditioning and conversion system (not shown) of the ultrasonictransducer 105.

According to an embodiment, the offline TOF method 200 _(A) comprisesdetermining the noise power NP of the electric echo signal EES (actionnode 210). According to an embodiment, the noise power NP of theelectric echo signal EES is determined at the Fourier module 115 of theprocessing unit 110.

According to an embodiment, the offline TOF method 200 _(A) comprisesdetermining the envelope signal EES_(ENV) (action node 215). Accordingto an embodiment, the envelope signal EES_(ENV) is determined at theHilbert module 120 of the processing unit 110.

According to an embodiment, the offline TOF method 200 _(A) comprisesdetermining the envelope signal portion EES_(ENVp) (action node 220).According to an embodiment, the envelope signal portion EES_(ENVp) isdetermined at the portion module 125 of the processing unit 110.According to an embodiment, the envelope signal portion EES_(ENVp) isdetermined based on the operative parameters OP_(k) resulting fromParticle Swarm Optimization performed at the previous ((k−1)-th)iteration before the current (k-th) iteration. According to anembodiment, at a first running of the offline TOF method 200 _(A) (k=0),in which no operative parameter tuning based on Particle SwarmOptimization has yet taken place, the operative parameters OP_(k) are atdefault values, the default values being for example determined atmanufacturer side, e.g., based on design experience.

According to an embodiment, the offline TOF method 200 _(A) comprisesdetermining the envelope signal estimate EES_(ENVest) according to theenvelope signal portion EES_(ENVp) and the noise power NP (action node225). According to an embodiment, the envelope signal portion EES_(ENVp)is determined at the UKF module 130 of the processing unit 110, asbetter discussed in the following with reference to FIG. 4. According toan embodiment, the envelope signal estimate EES_(ENVest) is determinedbased on the UKF parameters UKFP_(k) resulting from Particle SwarmOptimization performed at the previous ((k−1)-th) iteration before thecurrent (k-th) iteration. According to an embodiment, at a first runningof the offline TOF method 200 _(A) (k=0), in which no UKF parameterstuning based on Particle Swarm Optimization has yet taken place, the UKFparameters UKFP_(k) are at default values, the default values forexample being determined at manufacturer side, e.g., based on designerexperience.

According to an embodiment, the offline TOF method 200 _(A) comprisesdetermining the TOF estimate according to the envelope signal estimateEES_(ENVest) and the distance estimate D_(EST) according to the TOFestimate (action node 230). According to an embodiment, the TOF estimateand the distance estimate D_(EST) are determined at the evaluationmodule 135 of the processing unit 110.

According to an embodiment, the offline TOF method 200 _(A) comprisesdetermining the estimate error e as the difference between the distanceestimate D_(EST) and the distance D_(ACT) (action node 235A). Accordingto an embodiment, the estimate error e is determined at the error module140 of the processing unit 110.

According to an embodiment, the offline TOF method 200 _(A) comprisesiteratively adjusting and refining the operative parameters OP_(k) andthe UKF parameters UKFP_(k) (or a subset thereof) as long as theestimate error E is higher than a threshold estimate error ETH.According to an embodiment, if the estimate error E is higher than thethreshold estimate error ETH (exit branch N of decision node 240), thefollowing iteration starts (k=k+1, action node 245), and the operativeparameters OP_(k) and the UKF parameters UKFP_(k) are adjusted at theSwarm module 145 of the processing unit 110 based on the estimate errorE received as input by the error module 140 and on the Particle SwarmOptimization.

According to an embodiment, nodes 220-250 are repeated as such as longas the estimate error ε is higher than the threshold estimate error ETH.

Back to decision node 240, according to an embodiment, if the estimateerror ε is lower than the threshold estimate error ETH (exit branch Y ofdecision node 240), which means that the operative parameters OP_(k) andthe UKF parameters UKFP_(k) have been optimized, the optimized operativeparameters OP_(k) and the optimized UKF parameters UKFP_(k) are properlystored (action node 255) to be used for the following running of theoffline TOF method 200 _(A) (or for the subsequent running of the onlineTOF method), then the offline TOF method 200 _(A) ends.

The offline TOF method may be useful in the design phase, in which theTOF estimate is determined with great accuracy from a set of signals ina supervised manner and with known measurement conditions. The Applicanthas experimentally ascertained that a TOF device with operative and UKFparameters optimized through the offline TOF method is capable ofmanaging a very large number of shapes of ultrasonic echo signals fordistances within 0.30 m to 2 m, with a mean accuracy lower than 3 mm.

FIG. 2B shows an activity diagram of an online TOF method 200 _(B)according to embodiments of the present disclosure.

According to an embodiment, the online TOF method 200 _(B) isimplemented by proper software instructions stored in or accessible bythe TOF device 100, and/or by proper hardware/firmware of the TOF device100.

According to an embodiment, the online TOF method 200 _(B) comprisesacquiring the ultrasonic echo signal UES thereby obtaining thecorresponding electric echo signal EES (action node 205). According toan embodiment, the acquisition of the ultrasonic echo signal UES toobtain the corresponding electric echo signal EES is performed at theconditioning and conversion system (not shown) of the ultrasonictransducer 105.

According to an embodiment, the online TOF method 200 _(B) comprisesdetermining the noise power NP of the electric echo signal EES (actionnode 210). According to an embodiment, the noise power NP of theelectric echo signal EES is determined at the Fourier module 115 of theprocessing unit 110.

According to an embodiment, the online TOF method 200 _(B) comprisesdetermining the envelope signal EES_(ENV) (action node 215). Accordingto an embodiment, the envelope signal EES_(ENV) is determined at theHilbert module 120 of the processing unit 110.

According to an embodiment, the online TOF method 200 _(B) comprisesdetermining the envelope signal portion EES_(ENVp) (action node 220).According to an embodiment, the envelope signal portion EES_(ENVp) isdetermined at the portion module 125 of the processing unit 110.According to an embodiment, the envelope signal portion EES_(ENVp) isdetermined based on the subset of the operative parameters OP_(k)resulting from Particle Swarm Optimization performed at the previous((k−1)-th) iteration before the current (k-th) iteration. According toan embodiment, at a first running of the online TOF method 200 _(B)(k=0), in which no operative parameter tuning based on Particle SwarmOptimization has yet taken place, the subset of the operative parametersOP_(k) are at default values, the default values being for exampledetermined at manufacturer side, e.g., based on designer experience, orat an offline TOF method (such as the offline TOF method 200 _(A))performed before the online TOF method 200 _(B).

According to an embodiment, the subset of the operative parametersOP_(k) comprise, but are not limited to, the third and fourth operativeparameters Coeff1, Coeff2 (i.e., the operative parameters indicative ofoptimized lengths of the left and right portions of the envelope signalEES_(ENV), and hence of an optimized overall length of the envelopesignal portion EES_(ENV,p) over the abscissae axis). Indeed, theApplicant has experimentally ascertained that the first and secondoperative parameters Perc_l, Perc_u, especially when they are tunedduring an offline TOF method (such as the offline TOF method 200 _(A))preceding the online TOF method 200 _(B), are valid enough to allowidentifying (together with the third and fourth operative parametersCoeff1, Coeff2 tuned during the online method 200 _(B)) the bestenvelope signal portion EES_(ENV,p).

According to an embodiment, the online TOF method 200 _(B) comprisesdetermining the envelope signal estimate EES_(ENVest) according to theenvelope signal portion EES_(ENVp) and the noise power NP (action node225). According to an embodiment, the envelope signal portion EES_(ENVp)is determined at the UKF module 130 of the processing unit 110, asbetter discussed in the following with reference to FIG. 4. According toan embodiment, the envelope signal estimate EES_(ENVest) is determinedbased on the subset of the UKF parameters UKFP_(k) resulting fromParticle Swarm Optimization performed at the previous ((k−1)-th)iteration before the current (k-th) iteration. According to anembodiment, at a first running of the online TOF method 200 _(B) (k=0),in which no UKF parameters tuning based on Particle Swarm Optimizationhas yet taken place, the subset of the UKF parameters UKFP_(k) are atdefault values, the default values being for example determined atmanufacturer side, e.g., based on designer experience.

According to an embodiment, the subset of the UKF parameters UKFP_(k)comprise, but are not limited to, the evaluation parameter and thecontrol parameter. Indeed, the Applicant has experimentally ascertainedthat some parameters, such as the correction parameter, do not affect(or do not substantially affect) the TOF estimate. Therefore, accordingto an embodiment, the correction parameter, especially when it is tunedduring an offline TOF method (such as the offline TOF method 200 _(A))preceding the online TOF method 200 _(B), is not tuned again during theonline TOF method 200 _(B).

According to an embodiment, the online TOF method 200 _(B) comprisesdetermining the estimate error e as the difference between the envelopesignal estimate EES_(ENVest) and the envelope signal EES_(ENV) (actionnode 235B). According to an embodiment, the estimate error e isdetermined at the error module 140 of the processing unit 110.

According to an embodiment, the online TOF method 200 _(B) comprisesiteratively adjusting and refining the subset of the operativeparameters OP_(k) and the subset of the UKF parameters UKFP_(k) as longas the estimate error ε is higher than a threshold estimate errorε_(TH). According to an embodiment, if the estimate error ε is higherthan the threshold estimate error ε_(TH) (exit branch N of decision node240), the following iteration starts (k=k+1, action node 245), and thesubset of the operative parameters OP_(k) and the subset of the UKFparameters UKFP_(k) are adjusted at the Swarm module 145 of theprocessing unit 110 based on the estimate error ε received as input bythe error module 140 and on the Particle Swarm Optimization.

According to an embodiment, nodes 220-250 are repeated as such as longas the estimate error ε is higher than the threshold estimate errorε_(TH).

Back to decision node 240, according to an embodiment, if the estimateerror ε is lower than the threshold estimate error ε_(TH) (exit branch Yof decision node 240), which means that the subset of the operativeparameters OP_(k) and the subset of the UKF parameters UKFP_(k) havebeen optimized, the TOF estimate is determined according to the envelopesignal estimate EES_(ENVest) (i.e., the envelope signal estimateEES_(ENVest) determined based on the optimized subset of the operativeparameters OP_(k) and the optimized subset of the UKF parametersUKFP_(k)) and the distance estimate D_(EST) is determined according tothe TOF estimate (action node 230). According to an embodiment, the TOFestimate and the distance estimate D_(EST) are determined at theevaluation module 135 of the processing unit 110.

According to an embodiment, the optimized subset of the operativeparameters OP_(k) and the optimized subset of the UKF parametersUKFP_(k) are properly stored (action node 255) to be used for thefollowing running of the online TOF method 200 _(B).

The online TOF method provides a TOF estimate that dynamically andautomatically adapts to different external conditions.

As an example, the offline TOF method 200 _(A) and the online TOF method200 _(B) are carried out on a training dataset comprising, for example,160 ultrasonic echo signals (e.g., 135 used for the offline tuning and25 used for testing the online tuning) indicative of distances betweenthe target object T and the TOF device 100 ranging between about 0.3 mand about 2 m and acquired at a sampling rate of about 400 kS/s. Inparticular, the 25 ultrasonic echo signals used for testing the onlinetuning are acquired under various operative conditions (e.g., variabletemperature, humidity, wind speed, etc.).

Referring now to FIG. 3, it shows a simplified block diagram of anelectronic system 300 (i.e., a portion thereof) comprising the TOFdevice 100 (or more thereof) according to an embodiment of the presentdisclosure.

According to an embodiment, the electronic system 300 is suitable foruse in electronic apparatus.

According to an embodiment, the electronic system 300 comprises acontroller 305 (for example, one or more microprocessors and/or one ormore microcontrollers).

According to an embodiment, the electronic system 300 comprises aninput/output device 310 (for example, a keyboard and/or a screen). Theinput/output device 310 may for example be used to generate and/orreceive messages. The input/output device 310 may for example beconfigured to receive/supply a digital signal and/or an analog signal.

According to an embodiment, the electronic system 300 comprises awireless interface 315 for exchanging messages with a wirelesscommunication network (not shown), for example by means of radiofrequency signals. Examples of a wireless interface may include antennasand wireless transceivers.

According to an embodiment, the electronic system 300 comprises a powersupply device (for example, a battery 320) for powering the electronicsystem 300.

According to an embodiment, the controller 305 (or one or more dedicatedcomputing units, not shown) may be configured to determine additionalinformation (such as displacement information, level information,material information, structure information, vibration information, andmedical diagnostic information) based on the distance informationprovided by the TOF device 100.

According to an embodiment, the electronic system 300 comprises one morecommunication channels (bus) 325 to allow the exchange of data betweenthe TOF device 100, the controller 305 (when provided), the input/outputdevice 310 (when provided), the wireless interface 315 (when provided),and the power supply device 320 (when provided).

FIG. 4 shows an activity diagram of a signal cutting method 400according to embodiments of the present invention. The signal cuttingmethod 400 allows, during use of the TOF device 100, to determine theenvelope signal portion EES_(ENVp) by applying the operative parametersto the envelope signal EES_(ENV).

According to an embodiment, the signal cutting method 400 is implementedby proper software instructions stored in or accessible by the TOFdevice 100, and/or by proper hardware/firmware of the TOF device 100. Inparticular, the signal cutting method 400 is implemented by the portionmodule 125.

In details, the signal cutting method 400 comprises determining themaximum of the envelope signal EES_(ENV) (action node 405) according toper se known techniques, i.e. comprises calculating a maximum valuemax_inv of the envelope signal EES_(ENV) and a first temporal positionx_max_inv (over the abscissae axis, i.e. over time; also called firsttime instant x_max_inv) in the envelope signal EES_(ENV) of said maximumvalue max_inv.

The signal cutting method 400 comprises calculating, through the firstand the second operative parameter Perc_l, Perc_u, respectively a firstthreshold value thresh1 and a second threshold value thresh2 of theenvelope signal EES_(ENV) (action node 410), which are lower than themaximum value max_inv. In details, the first threshold value thresh1 isa threshold for the envelope signal EES_(ENV) that is related to amaximum length of the left portion of the envelope signal EES_(ENV), andthe second threshold value thresh2 is a threshold for the envelopesignal EES_(ENV) that is related to a maximum length of the rightportion of the envelope signal EES_(ENV). In other words, the leftportion of the envelope signal EES_(ENV) comprises values of theenvelope signal EES_(ENV) ranging between the first threshold valuethresh1 and the maximum value max_inv, and the right portion of theenvelope signal EES_(ENV) comprises values of the envelope signalEES_(ENV) ranging between the maximum value max_inv and the secondthreshold value thresh2. In further details, the first threshold valuethresh1 and the second threshold value thresh2 are calculated based on(in particular, dependent on the product of) the maximum value max_invof the envelope signal EES_(ENV) and the first and, respectively, thesecond operative parameter Perc_l, Perc_u. In particular,thresh1=Perc_l·max_inv and thresh2=Perc_u·max_inv.

The signal cutting method 400 further comprises determining a secondtemporal position x_thresh1 (over the abscissae axis, i.e. over time;also called second time instant x_max_inv) of the first threshold valuethresh1 and a third temporal position x_thresh2 (over the abscissaeaxis, i.e. over time; also called third time instant x_max_inv) of thesecond threshold value thresh2 in the envelope signal EES_(ENV) (actionnode 415), the second and third temporal position x_thresh1, x_thresh2being related to a maximum length of the left and, respectively, rightportion of the envelope signal EES_(ENV). The first and second temporalpositions x_thresh1, x_thresh2 are the temporal positions of theenvelope signal EES_(ENV) at which the envelope signal EES_(ENV) assumesthe first and, respectively, the second threshold values thresh1,thresh2 and are positioned prior to and, respectively, following thefirst temporal position x_max_inv (i.e., are at left and, respectively,at right of the first temporal position x_max_inv on the time scale). Inother words, x_thresh1=x_max_inv−Δ1 and x_thresh2=x_max_inv−Δ2, where Δ1and Δ2 are respective time intervals whose lengths correspond to themaximum time lengths of the left and, respectively, right portions ofthe envelope signal EES_(ENV). Therefore, the left portion of theenvelope signal EES_(ENV) ranges at most between the second temporalposition x_thresh1 and the first temporal position x_max_inv, and theright portion of the envelope signal EES_(ENV) ranges at most betweenthe first temporal position x_max_inv and the third temporal positionx_thresh2.

Therefore, according to an embodiment, the envelope signal portionEES_(ENVp) includes (in details, coincides with) the portion of theenvelope signal EES_(ENV) comprised between the second temporal positionx_thresh1 and the third temporal position x_thresh2.

Optionally, according to a different embodiment, the signal cuttingmethod 400 further comprises optimizing the envelope signal portionEES_(ENVp) by calculating, through the third and fourth operativeparameters Coeff1, Coeff2, a fourth temporal position x_opt1 (over theabscissae axis, i.e. over time; also called fourth time instantx_max_inv) and a fifth temporal position x_opt2 (over the abscissaeaxis, i.e. over time; also called fifth time instant x_max_inv) of theenvelope signal EES_(ENV) (action node 420). The fourth temporalposition x_opt1 is greater than the second temporal position x_thresh1and depends on the second temporal position x_thresh1 and the thirdoperative parameter Coeff1, and the fifth temporal position x_opt2 islower than the third temporal position x_thresh2 and depends on thethird temporal position x_thresh2 and the fourth operative parameterCoeff2. In the present embodiment, the envelope signal portionEES_(ENVp) includes (in details, coincides with) the portion of theenvelope signal EES_(ENV) comprised between the fourth temporal positionx_opt1 and the fifth temporal position x_opt2. In other words, thefourth temporal position x_opt1 and the fifth temporal position x_opt2are indicative of an optimized length of the left and, respectively, ofthe right portion of the envelope signal EES_(ENV). In particular,x_thresh1<x_opt1<x_max_inv and x_max_inv<x_opt2<x_thresh2. In furtherdetails, the third and fourth operative parameters Coeff1, Coeff2 areindicative of respective time offsets to be applied respectively to thesecond and third temporal positions x_thresh1, x_thresh2 to obtain thefourth and fifth temporal position x_opt1, x_opt2. In other words,x_opt1=x_thresh1+Coeff1 and x_opt2=x_thresh2−Coeff2, where Coeff1<Δ1 andCoeff2<Δ2. Therefore, in the present case the optimized left portion ofthe envelope signal EES_(ENV) ranges between the fourth temporalposition x_opt1 and the first temporal position x_max_inv, and theoptimized right portion of the envelope signal EES_(ENV) ranges betweenthe first temporal position x_max_inv and the fifth temporal positionx_opt2.

FIG. 5 shows an activity diagram of a time-of-flight estimation method500 according to embodiments of the present invention. Thetime-of-flight estimation method 500 allows to estimate thetime-of-flight between the ultrasonic signal emitted by the TOF device100 and the ultrasonic echo signal returned by the target object T hitby the ultrasonic signal and received at the TOF device 100.

According to an embodiment, the time-of-flight estimation method 500 isimplemented by proper software instructions stored in or accessible bythe TOF device 100, and/or by proper hardware/firmware of the TOF device100.

According to an embodiment, the time-of-flight estimation method 500comprises acquiring the ultrasonic echo signal UES thereby obtaining thecorresponding electric echo signal EES (action node 505). According toan embodiment, the acquisition of the ultrasonic echo signal UES toobtain the corresponding electric echo signal EES is performed at theconditioning and conversion system (not shown) of the ultrasonictransducer 105.

According to an embodiment, the time-of-flight estimation method 500comprises determining the noise power NP of the electric echo signal EES(action node 510). According to an embodiment, the noise power NP of theelectric echo signal EES is determined at the Fourier module 115 of theprocessing unit 110.

According to an embodiment, the time-of-flight estimation method 500comprises determining the envelope signal EES_(ENV) (action node 515).According to an embodiment, the envelope signal EES_(ENV) is determinedat the Hilbert module 120 of the processing unit 110.

According to an embodiment, the time-of-flight estimation method 500comprises determining the envelope signal portion EES_(ENV,p) (actionnode 400) according to the previously described signal cutting method400. According to an embodiment, the envelope signal portion EES_(ENV,p)is determined at the portion module 125 of the processing unit 110 basedon the operative parameters OP_(k) resulting from the offline TOF method200 _(A) or the online TOF method 200 _(B).

According to an embodiment, the time-of-flight estimation method 500comprises determining the envelope signal estimate EES_(ENV,est)according to the envelope signal portion EES_(ENV,p) and the noise powerNP (action node 525). According to an embodiment, the envelope signalportion EES_(ENV,p) is determined at the UKF module 130 of theprocessing unit 110 based on the UKF parameters UKFP_(k) resulting fromthe offline TOF method 200 _(A) or the online TOF method 200 _(B).

According to an embodiment, the time-of-flight estimation method 500comprises determining the TOF estimate according to the envelope signalestimate EES_(ENV,est) and the distance estimate D_(EST) according tothe TOF estimate (action node 530). According to an embodiment, the TOFestimate and the distance estimate D_(EST) are determined at theevaluation module 135 of the processing unit 110.

From an examination of the characteristics of the invention madeaccording to the present invention, the advantages that it allows areevident.

The signal cutting method 400 reduces the computational cost requiredfor estimating the time-of-flight through the time-of-flight estimationmethod 500, in particular by reducing the computation cost of the UKFmodule 130.

The envelope signal portion EES_(ENV,p) outputted by the action node 415is already optimized for the UKF module 130. Nevertheless, calculatingthe envelope signal portion EES_(ENV,p) according to the action node 420allows to further reduce the computational cost and improve thetime-of-flight estimation accuracy.

The optimized subset of the operative parameters OP_(k) and theoptimized subset of the UKF parameters UKFP_(k) are calculated throughthe offline TOF method 200 _(A). Moreover, the third and fourthoperative parameters Coeff1, Coeff2 can be further optimized through theonline TOF method 200 _(B).

Finally, it is clear that modifications and variations may be made tothe invention described and illustrated herein without thereby departingfrom the scope of the present invention, as defined in the attachedclaims. For example, the different embodiments described may be combinedto provide further solutions.

Method (200 _(A); 200 _(B)) for providing an estimate of atime-of-flight between an ultrasonic signal emitted by a device (100)and an ultrasonic echo signal returned by a target object (T) hit by theultrasonic signal and received at the device, the method may besummarized as including acquiring (205) the ultrasonic echo signalthereby obtaining an electric echo signal; determining (210) a noisepower of the electric echo signal; determining (215) an envelope signalindicative of an envelope of the electric echo signal; determining (220)a portion of the envelope signal based on at least one operativeparameter (OPK), said at least one operative parameter being determinedaccording to Particle Swarm Optimization; processing (225) the portionof the envelope signal and the noise power of the electric echo signalaccording to an Unscented Kalman Filter to obtain an estimate of theenvelope signal, wherein the estimate of the envelope signal is aregenerated version of the envelope signal being regenerated from theportion of the envelope signal, said processing being based on at leastone Unscented Kalman Filter parameter (UKFPK) determined according tothe Particle Swarm Optimization, and providing (230) said estimate ofthe time-of-flight according to the estimate of the envelope signal.

Method (200A; 200B) may further include determining (235A; 235B) anestimate error, wherein the Particle Swarm Optimization is based on saidestimate error.

Said determining (235A; 235B) an estimate error may include determining(235B) a difference between the estimate of envelope signal and theenvelope signal.

Method (200A; 200B) may further include, based on said estimate of thetime-of-flight, determining (230) a distance estimate indicative of adistance between the target object (T) and the device (100), whereinsaid determining (235A; 235B) an estimate error comprises determining(235A) a difference between the distance estimate and said distance.

Said determining (215) an envelope signal may include performing aHilbert transformation on the electric echo signal.

The portion of the envelope signal may be centered about a maximum valueof the envelope signal.

Said processing (225) the operative portion of the envelope signal mayinclude providing a regenerated envelope signal.

Said at least one operative parameter (OPK) may include at least oneamong operative parameters indicative of a maximum length of the portionof the envelope signal over time, and operative parameters indicative ofan optimized length of the portion of the envelope signal over time, theoptimized length being lower than the maximum length.

Said at least one Unscented Kalman Filter parameter (UKFPK) may includeat least one among an evaluation parameter providing a rough estimate ofthe time-of-flight; a control parameter for controlling the spread ofsigma points around a mean state value, and a correction parameterproviding a correction on the noise power of the electric echo signal.

Device (100) for providing an estimate of a time-of-flight between anultrasonic signal emitted by the device and an ultrasonic echo signalreturned by a target object (T) hit by the ultrasonic signal andreceived at the device, the device may be summarized as including aconditioning and conversion system (105) for acquiring the ultrasonicecho signal thereby obtaining an electric echo signal; a module (115)for determining a noise power of the electric echo signal; a module(120) for determining an envelope signal indicative of an envelope ofthe electric echo signal; a module (125) for determining a portion ofthe envelope signal based on at least one operative parameter (OPK),said at least one operative parameter being determined according toParticle Swarm Optimization; a module (130) for processing the portionof the envelope signal and the noise power of the echo ultrasonic signalaccording to an Unscented Kalman Filter to obtain an estimate of theenvelope signal, wherein the estimate of the envelope signal is aregenerated version of the envelope signal being regenerated from theportion of the envelope signal, said processing being based on at leastone Unscented Kalman Filter parameter (UKFPK) determined according tothe Particle Swarm Optimization, and a module (135) for providing saidestimate of the time-of-flight according to the estimate of the envelopesignal.

The various embodiments described above can be combined to providefurther embodiments. Aspects of the embodiments can be modified, ifnecessary, to employ concepts of the various embodiments to provide yetfurther embodiments.

These and other changes can be made to the embodiments in light of theabove-detailed description. In general, in the following claims, theterms used should not be construed to limit the claims to the specificembodiments disclosed in the specification and the claims, but should beconstrued to include all possible embodiments along with the full scopeof equivalents to which such claims are entitled. Accordingly, theclaims are not limited by the disclosure.

1. A method for providing an estimate of a time-of-flight between anultrasonic signal emitted by a device and an ultrasonic echo signalreturned from a target object hit by the ultrasonic signal and receivedat the device, the method comprising: acquiring the ultrasonic echosignal; obtaining an electric echo signal based on the ultrasonic echosignal; determining a noise power of the electric echo signal;determining an envelope signal indicative of an envelope of the electricecho signal; determining at least one operative parameter of the envelopsignal based on Particle Swarm Optimization; determining a portion ofthe envelope signal based on the at least one operative parameter;determining at least one Unscented Kalman Filter parameter of anUnscented Kalman Filter according to Particle Swarm Optimization;processing the portion of the envelope signal and the noise power of theelectric echo signal according to the Unscented Kalman Filter to obtainan estimate of the envelope signal, the estimate of the envelope signalbeing a regenerated version of the envelope signal based on the portionof the envelope signal; and providing the estimate of the time-of-flightaccording to the estimate of the envelope signal.
 2. The methodaccording to claim 1, further comprising: determining an estimate errorbetween the estimate of the envelop signal and the envelop signal; anddetermining the Particle Swarm Optimization based on the estimate error.3. The method according to claim 2, wherein the determining an estimateerror comprises determining a difference between the estimate ofenvelope signal and the envelope signal.
 4. The method according toclaim 2, further comprising, based on the estimate of thetime-of-flight, determining a distance estimate indicative of a distancebetween the target object and the device, wherein the determining anestimate error comprises determining a difference between the distanceestimate and the distance.
 5. The method according to claim 1, whereinthe determining the envelope signal comprises performing a Hilberttransformation on the electric echo signal.
 6. The method according toclaim 1, wherein the portion of the envelope signal is centered about amaximum value of the envelope signal.
 7. The method according to claim1, wherein the processing the portion of the envelope signal comprisesproviding a regenerated envelope signal.
 8. The method according toclaim 1, wherein the at least one operative parameter comprises at leastone among: a first and a second operative parameters indicative of amaximum length of the portion of the envelope signal over time, or athird and a fourth operative parameters indicative of an optimizedlength of the portion of the envelope signal over time, the optimizedlength being lower than the maximum length.
 9. The method according toclaim 8, wherein said determining the portion of the envelope signalcomprises: calculating a maximum value of the envelope signal and acorresponding first temporal position at which the envelope signal hasthe maximum value; calculating, as a function of the first and secondoperative parameters and of the maximum value of the envelope signal, afirst and, respectively, a second threshold value of the envelopesignal, the first and the second threshold values being lower than themaximum value of the envelope signal; and determining a second temporalposition of the envelop signal at which the value of the envelop signalis equal to the first threshold value, and a third temporal position ofthe envelop signal at which the value of the envelop signal is equal tothe second threshold value, the second temporal position being lowerthan the first temporal position and the third temporal position beinggreater than the first temporal position.
 10. The method according toclaim 9, wherein said portion of the envelope signal is defined betweenthe second temporal position and the third temporal position.
 11. Themethod according to claim 9, wherein said determining the portion of theenvelope signal further comprises calculating a fourth temporal positionas a function of the third operative parameter and of the secondtemporal position, and a fifth temporal position as a function of thefourth operative parameter and of the third temporal position, thefourth temporal position being greater than the second temporal positionand lower than the first temporal position and the fifth temporalposition being greater than the first temporal position and lower thanthe third temporal position, and wherein said portion of the envelopesignal is defined between the fourth temporal position and the fifthtemporal position.
 12. The method according to claim 1, wherein the atleast one Unscented Kalman Filter parameter comprises at least oneamong: an evaluation parameter providing a rough estimate of thetime-of-flight; a control parameter for controlling spread of sigmapoints around a mean state value, and a correction parameter providing acorrection on the noise power of the electric echo signal.
 13. A devicefor providing an estimate of a time-of-flight between an ultrasonicsignal emitted by the device and an ultrasonic echo signal returned by atarget object hit by the ultrasonic signal and received at the device,the device comprising: a conditioning and conversion system foracquiring the ultrasonic echo signal and obtaining an electric echosignal based on the ultrasonic echo signal; circuitry for determining anoise power of the electric echo signal; circuitry for determining anenvelope signal indicative of an envelope of the electric echo signal;circuitry for determining a portion of the envelope signal based on atleast one operative parameter of the envelop signal, the at least oneoperative parameter being determined according to Particle SwarmOptimization; circuitry for obtaining an estimate of the envelope signalby processing the portion of the envelope signal and the noise power ofthe echo ultrasonic signal according to an Unscented Kalman Filter, theestimate of the envelope signal being a regenerated version of theenvelope signal based on the portion of the envelope signal and at leastone Unscented Kalman Filter parameter determined according to theParticle Swarm Optimization, and circuitry for providing the estimate ofthe time-of-flight according to the estimate of the envelope signal. 14.The device according to claim 13, further comprising: circuitry fordetermining an estimate error between the estimate of the envelop signaland the envelop signal; and circuitry for determining the Particle SwarmOptimization based on the estimate error.
 15. A method, comprising:obtaining an electric echo signal representing a time-of-flight signalfrom a device and reflected back from an object; determining a noisepower of the electric echo signal; generating a first envelope signalindicative of an envelope of the electric echo signal; determining aportion of the first envelope signal; generating a second envelop signalbased on the portion of the first envelope signal and the noise power ofthe electric echo signal using an Unscented Kalman Filter; andcalculating a travel time of the time-of-flight signal based on thesecond envelop signal.
 16. The method of claim 15, comprising:determining an operative parameter of the first envelop signal based onParticle Swarm Optimization, wherein the determining the portion of thefirst envelope signal includes determining the portion of the firstenvelope signal based on the operative parameter.
 17. The method ofclaim 15, comprising determining a parameter of the Unscented KalmanFilter based on Particle Swarm Optimization.
 18. The method according toclaim 15, comprising: determining an estimated distance indicative of adistance between the object and the device based on the travel time; anddetermining a difference between the estimated distance and thedistance.
 19. The method according to claim 15 wherein the determiningthe first envelope signal includes performing a Hilbert transformationon the electric echo signal.
 20. The method according to claim 15wherein the determining the portion of the first envelope signalincludes determining the portion of the first envelope signal that iscentered at a maximum value of the first envelope signal.